15 papers
Multi-Alignment Contrastive Learning for Enzyme--Reaction Retrieval
Gengmo Zhou, Feng Yu, Wenda Wang +4
Identifying enzymes that catalyze target biochemical reactions is a key step in computational enzyme discovery and biocatalyst design. Recent representation-learning methods formul…
NOSE: Neural Olfactory-Semantic Embedding with Tri-Modal Orthogonal Contrastive Learning
Yanyi Su, Hongshuai Wang, Zhifeng Gao +1
Olfaction lies at the intersection of chemical structure, neural encoding, and linguistic perception, yet existing representation methods fail to fully capture this pathway. Curren…
ProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design
Yutang Ge, Guojiang Zhao, Sihang Li +7
Designing proteins that satisfy natural language functional requirements is a central goal in protein engineering. A straightforward baseline is to fine-tune generic instruction-tu…
Scaffold-Conditioned Preference Triplets for Controllable Molecular Optimization with Large Language Models
Yi Xiong, Liang Xiong, Xiaohong Ji +4
Molecular property optimization is central to drug discovery, yet many deep learning methods rely on black-box scoring and offer limited control over scaffold preservation, often p…
MolReasoner: Toward Effective and Interpretable Reasoning for Molecular LLMs
Guojiang Zhao, Zixiang Lu, Yutang Ge +13
Large Language Models (LLMs) have shown impressive performance across various domains, but their ability to perform molecular reasoning remains underexplored. Existing methods most…
On the Design of One-step Diffusion via Shortcutting Flow Paths
Haitao Lin, Peiyan Hu, Minsi Ren +5
Recent advances in few-step diffusion models have demonstrated their efficiency and effectiveness by shortcutting the probabilistic paths of diffusion models, especially in trainin…